Preserves is a new data language designed for clarity and expressiveness, aiming to bridge the gap between simple configuration formats like JSON/YAML and full-fledged programming languages. It focuses on data transformation and manipulation with a concise syntax inspired by functional programming. Key features include immutability, a type system emphasizing structural types, built-in support for common data structures like maps and lists, and user-defined functions for more complex logic. The project aims to offer a powerful yet approachable tool for tasks ranging from simple configuration to data processing and analysis, especially where maintainability and readability are paramount.
The blog post, "Preserves: An Expressive Data Language," introduces Preserves, a novel data description language designed for enhanced clarity, maintainability, and expressiveness in managing complex data structures, particularly in configuration files and data interchange formats. The authors argue that existing data languages like JSON, YAML, and TOML, while widely used, often lack the robustness required for intricate data scenarios, leading to difficulties in validation, documentation, and evolution as projects scale. Preserves addresses these shortcomings by incorporating several key features.
First and foremost, Preserves emphasizes strong typing through a rich type system, encompassing not just basic types like strings, numbers, and booleans, but also more complex constructs such as enums, tuples, lists, maps, and even user-defined types. This strict typing allows for early error detection and improved code maintainability by providing clear expectations about the data structure. Furthermore, it facilitates automated documentation generation and enables advanced tooling for data validation and manipulation.
The language also embraces the concept of constraints, allowing developers to specify detailed rules about the permissible values within the data structures. These constraints can range from simple range checks on numerical values to more sophisticated pattern matching on strings and even cross-field validation, ensuring data integrity and reducing the potential for runtime errors caused by unexpected data. This granular control over data validity is a significant departure from the more permissive nature of many existing data languages.
Beyond its core type system and constraints, Preserves boasts features aimed at maximizing expressiveness and reducing boilerplate. The language supports the definition of reusable types, allowing developers to create custom data structures that can be referenced throughout their projects. This promotes modularity and consistency, simplifying the management of complex data schemas. Preserves also incorporates the notion of default values, which can be specified for fields within a type definition, reducing the need to explicitly define every value in every instance and simplifying data entry.
Importantly, Preserves is designed with tooling in mind. The post highlights the potential for robust tools built around the language, including validators, formatters, and even code generators, all leveraging the rich type information and constraints embedded within Preserves definitions. This focus on tooling underscores the practical applicability of the language and its potential to improve the developer experience in managing data-intensive projects.
In summary, Preserves seeks to transcend the limitations of existing data languages by offering a strongly typed, constraint-driven approach to data definition. Its emphasis on expressiveness, coupled with its focus on tooling, positions it as a promising solution for managing complex data structures in a more robust and maintainable manner. The authors believe Preserves provides a powerful new tool for developers striving for better data management across a variety of applications.
Summary of Comments ( 25 )
https://news.ycombinator.com/item?id=42864122
Hacker News users discussed Preserves' potential, comparing it to tools like JSON, YAML, TOML, and edn. Some lauded its expressiveness, particularly its support for comments and arbitrary keys. Others questioned its practical value beyond configuration files, wondering about performance, tooling, and whether its added complexity justified the benefits over simpler formats. The lack of a formal specification was also a concern. Several commenters expressed interest in seeing real-world use cases and benchmarks to better assess Preserves' viability. Some saw potential for niche applications like game modding or creative coding, while others remained skeptical about its broad adoption. The discussion highlighted the trade-off between expressiveness and simplicity in data languages.
The Hacker News discussion on "Preserves: An Expressive Data Language" contains several interesting comments exploring different facets of the language and its potential applications.
Several commenters discuss the similarities and differences between Preserves and other data languages or tools. One commenter points out the resemblance to Nix, highlighting the functional nature and immutability aspects shared by both. This comparison sparks a sub-thread discussing the relative merits and trade-offs of each. Another commenter draws parallels to Dhall, another configuration language emphasizing type safety, and questions how Preserves differentiates itself. This leads to a discussion of Preserves' focus on ease of use and a more streamlined syntax compared to Dhall. Further comparisons are made to CUE and Jsonnet, with commenters analyzing the different approaches to data templating and validation offered by each language.
The topic of performance also arises, with one commenter inquiring about the runtime performance characteristics of Preserves. Another user raises concerns about the potential for increased complexity when dealing with larger projects, questioning whether Preserves can maintain its simplicity in such scenarios. This prompts a discussion on the importance of proper tooling and organizational practices to mitigate these challenges.
Some comments focus on the practical applications of Preserves. One commenter expresses interest in using it for managing Kubernetes configurations, suggesting that its declarative nature and immutability could be beneficial in this context. Another user discusses the potential of using Preserves for infrastructure as code, highlighting the advantages of a type-safe and expressive language for defining and managing infrastructure resources.
A few commenters delve into the technical aspects of Preserves, inquiring about its type system and the underlying implementation. One comment specifically asks about the support for higher-kinded types and how they are handled within the language. This leads to a brief explanation of Preserves' type system and its capabilities.
Overall, the comments section reveals a generally positive reception towards Preserves, with many expressing interest in exploring its capabilities further. However, some concerns are raised regarding performance, scalability, and the potential learning curve associated with a new data language. The discussion offers valuable insights into the potential strengths and weaknesses of Preserves and its place within the broader ecosystem of data management and configuration tools.